Abstract
Many physical processes are postulated to obey a monotonic relationship, whereby the entity of interest must strictly increase or decrease as a function of a covariate. Monotonic polynomials are a popular tool for incorporating such a priori knowledge into statistical models, particularly in settings where irreducible noise induces unconstrained model fits which violate the presupposed monotonic relationship. This thesis develops novel Bayesian methodologies for fitting monotonic polynomials and selecting the appropriate polynomial model, in a variety of scenarios.
Original language | English |
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Qualification | Masters |
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Award date | 13 Jun 2018 |
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Publication status | Unpublished - 2018 |